Examples of Nutrition ClaimsClaims about a popular diet

Examples of Nutrition Claims
Claims about a popular diet that is supposed to change your
body, reverse a disease, or dramatically improve your health or
performance in some way.
Claims about a particular food, beverage or dietary supplement
that is supposed to help you lose weight, gain muscle, boost
immunity, improve mood or memory, lower blood cholesterol or
blood sugar levels, fight inflammation, remove toxins, prevent
or cure a disease, make your hair/nails/skin/digestion better,
slow aging…
Claims about a particular ingredient in foods/beverages that’s
supposed to be “bad,” “toxic,” or contribute to a particular
health problem (acne, autism, ADHD, PCOS, diabetes, cancer,
Alzheimer’s disease, aging, hormone disruption, infertility,
obesity, digestive problems)…
Sources of Nutrition Claims
Google!
Magazines, Newspapers, Blogs
Books, Videos, Documentaries
Advertisements, Social Media Influencers
Product label, brochure, website
Scientific peer-reviewed journals

How to choose a claim…
Examples:
Magazine article or blog claiming Intermittent fasting or whole
30 or keto is the answer to weight loss. Vitamin D/C/zinc and
COVID19.
LA Times article reporting on a new study that shows chocolate
or red wine protects the heart (in time for Valentine’s Day)
Book or Youtube video that claims sugar or wheat or gluten is
toxic
Documentary that claims plant-based diet best for performance
(The Game Changers)
Advertisement about new dietary supplement or “cleanse” for
brain health, skin health, digestive health (turmeric, collagen,
probiotics, spirulina, apple cider vinegar)
Website bodybuilding.com claiming need certain amount/type
of protein to get huge muscles. Or no soy/no dairy for PCOS or
fertility.
2
Evaluating Nutrition Research & Claims
Is the source credible & unbiased?
Author/credentials
“Nutritionists” vs. “Registered Dietitians” – what’s the
difference?
Self-proclaimed guru, fitness trainer, massage therapist, store
clerk
MDs, DCs, PhDs – are they always reliable?
Is there any conflict of interest? Are they trying to sell you
something?
Publication source
Internet site (.com or .org, .edu, .gov)
Magazine, newsletter, brochure, trade journal (paid advertising)
Peer-reviewed, professional/scientific journal

Most “nutritionists” have little to no formal education/degree
(e.g. famous people, fitness trainer/massage therapist/GNC or
health food store clerk). Some “nutritionists” do have a high
level of education/degree, but they may or may not be highly
educated in nutrition
Conflict of interest – Juice plus, herbal life, arbonne sales rep
directly trying to sell you something or
researcher/author/speaker could be employed/paid by the
company trying to sell something (funded by beef/dairy council)
Example of ephedra article in fitness magazine, local SCV
magazines
3
How good is the research?
Study design
No systematic method at all
testimonials, anecdotal, before/after
Epidemiological / Observational Studies
only show correlations, NOT cause and effect (due to
confounding variables)
Intervention / Experimental Studies
CAN show cause & effect (because confounding variables are
controlled)
Best if study 1) randomized, 2) placebo-controlled, AND 3)
double-blinded
# of subjects / type of subjects
Study length
Especially when looking at long term weight loss and
health/safety

Epidemiological: Look at population of people’s lifestyle
habits and look for associations/correlations with health/disease
outcomes.
Ex. Study shows eating ice cream is associated with drowning
(that doesn’t show eating ice cream causes drowning…what
confounding factor might be the reason for the association?
People tend to eat more ice cream in the summer. People also
tend to swim more in the summer.)
Ex. Poor sperm quality linked to more phone/laptop use at
night. Confounding factor is less quality sleep linked with
both.
Ex: People who drink red wine have less heart disease (based on
Mediterranean diet, but could be less sat fat red meat, more
olive oil MUFAs, more fruits/vegs, more active lifestyle, and/or
genetics/ethnicity)
Ex: Americans eating more carbs and gaining weight (but
americans also eating more calories!)
Intervention: Give a group of people an intervention/treatment
and then see what effect it has on health/disease outcome.
Ex: Hydroxycut/diet pill 8 week placebo-controlled study with
same diet/exercise (but not randomized)
Caffeine revs metabolism initially but effects wear off, no
effect on weight/body fat loss. Long term keto diet effects?
Subjects: Animals/rats vs. humans., Post menopausal women
vs. young men (soy), congestive heart failure pts. Vs. young
men (arginine/nitric oxide supplements)
4

Be cautious with Observational Studies
Observational studies do NOT prove one thing causes the other.
They only show that two things are associated with one another.
The REAL cause may be due to a confounding variable that is
associated with both things.
NOTE: In this example, poor quality sleep is the confounding
variable since it results from late night electronics and is the
real cause of poor sperm quality.
Are the findings put in perspective?
How many studies show a positive vs. negative or “null” effect?
Any warning about adverse side effects/risks?
Is the effective “dose” the same as what’s commonly consumed
in a food or supplement?
Any acknowledgement of other factors that have a more
significant impact on the outcome than the one being studied?
e.g. Risk of getting cancer from NOT eating vegetables is far
greater than the cancer risk from ingesting trace amounts of
pesticide residues on those vegetables.
# studies: Anyone can “cherry-pick” studies to find only those 2
that support the claim (without mentioning the hundreds that
don’t)

Safety: Keto and intermittent fasting in certain populations
harmful
Reasonable dose: omega 3s DHA added to foods
Physiologically significant: pesticide resides may be higher in
conventional than organic, but the cancer risk of NOT eating
any fruits and veggies is far greater
6
Using Library Resources
www.aging-us.com 2764 AGING
INTRODUCTION
Major depressive disorder (MDD) is viewed as a major
public health problem globally. MDD has a substantial
impact on society and individuals, such as increasing
economic burden and decreasing labor productivity
[1–3]. At a global level, more than 300 million people
are estimated to suffer from MDD, which is equivalent

to 4.4% of the world’s population [4]. However, the
pathogenesis of MDD is still unclear. Some theories
have been developed to explain the biological
mechanisms of MDD, such as neurotrophic alterations
www.aging-us.com AGING 2020, Vol. 12, No. 3
Research Paper
Age-specific differential changes on gut microbiota composition
in
patients with major depressive disorder
Jian-Jun Chen1,2,*,#, Sirong He3,*, Liang Fang2,4,*, Bin
Wang1, Shun-Jie Bai5, Jing Xie6, Chan-Juan
Zhou7, Wei Wang8, Peng Xie4,7,8,#
1Institute of Life Sciences, Chongqing Medical University,
Chongqing 400016, China
2Chongqing Key Laboratory of Cerebral Vascular Disease
Research, Chongqing Medical University, Chongqing
400016, China
3Department of Immunology, College of Basic Medicine,
Chongqing Medical University, Chongqing 400016, China
4Department of Neurology, Yongchuan Hospital of Chongqing
Medical University, Chongqing 402160, China
5Department of Laboratory, The First Affiliated Hospital of
6Department of Endocrinology and Nephrology, Chongqing
University Central Hospital, Chongqing Emergency
Medical Center, Chongqing 400014, China
7NHC Key Laboratory of Diagnosis and Treatment on Brain

Functional Diseases, Chongqing Medical University,
Chongqing 400016, China
8Department of Neurology, The First Affiliated Hospital of
*Equal contribution
#Co-senior authors
Correspondence to: Peng Xie, Jian-Jun Chen; email:
[email protected], [email protected]
Keywords: major depressive disorder, gut microbiota,
Firmicutes, Bacteroidetes, Actinobacteria
Received: November 21, 2019 Accepted: January 12, 2020
Published: February 10, 2020
Copyright: Chen et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution License
(CC BY 3.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original author and
source are credited.
ABSTRACT
Emerging evidence has shown the age-related changes in gut
microbiota, but few studies were conducted to
explore the effects of age on the gut microbiota in patients with
major depressive disorder (MDD). This study was
performed to identify the age-specific differential gut
microbiota in MDD patients. In total, 70 MDD patients and 71
healthy controls (HCs) were recruited and divided into two
groups: young group (age 18-29 years) and middle-aged
group (age 30-59 years). The 16S rRNA gene sequences were
extracted from the collected fecal samples. Finally, we

found that the relative abundances of Firmicutes and
Bacteroidetes were significantly decreased and increased,
respectively, in young MDD patients as compared with young
HCs, and the relative abundances of Bacteroidetes
and Actinobacteria were significantly decreased and increased,
respectively, in middle-aged MDD patients as
compared with middle-aged HCs. Meanwhile, six and 25
differentially abundant bacterial taxa responsible for the
differences between MDD patients (young and middle-aged,
respectively) and their respective HCs were identified.
Our results demonstrated that there were age-specific
differential changes on gut microbiota composition in
patients with MDD. Our findings would provide a novel
perspective to uncover the pathogenesis underlying MDD.
mailto:[email protected]
and neurotransmission deficiency [5, 6]. However, none
of these theories has been universally accepted.
Therefore, there is a pressing need to identify novel
pathophysiologic mechanisms underlying this disease.
In recent years, mounting evidence has shown that gut
microbiota could play a vital role in every aspect of
physiology [7]. It is the largest and most direct external

environment of humans. Previous studies found that the
disturbance of gut microbiota had a crucial role in the
pathogenesis of many diseases [8–10]. Recent studies
reported that gut microbiota could affect the host brain
function and host behaviors through microbiota-gut-
brain axis [11, 12]. Using germ-free mice, we found that
gut microbiota could influence the gene levels in the
hippocampus of mice and lipid metabolism in the
prefrontal cortex of mice [13, 14]. Our clinical studies
demonstrated that the disturbance of gut microbiota
might be a contributory factor in the development of
MDD [15, 16].
Nowadays, emerging evidence has shown the age-
related changes in gut microbiota composition. For
example, Firmicutes is the dominant taxa during the
neonatal period, but Actinobacteria and Proteobacteria
are about to increase in three to six months [17]. While

in adults, Vemuri et al. reported that Bacteroidetes and
Firmicutes were the dominant taxa [18]. Meanwhile,
compared to younger individuals, the abundance of
Bacteroidetes is significantly higher in frailer older
individuals [19]. These results showed that there was a
close relationship between age and gut microbiota
composition. Ignoring this relationship would affect the
robust of results when exploring the mechanism of
action of gut microbiota in diseases. Therefore, to study
the relationship between gut microbiota and MDD
patients in different age groups, we recruited 52 young
subjects aged from 18 to 29 years (27 healthy controls
(HCs) and 25 MDD patients) and 89 middle-aged
subjects aged from 30 to 59 years (44 HCs and 45 MDD
patients). The main purpose of this study was to identify
the age-specific differential changes on gut microbiota
composition in MDD patients. Our results would
display the different changes of gut microbiota

composition along with age between HCs and MDD
patients.
RESULTS
Differential gut microbiota composition
As shown in Figure 1, the results of abundance-based
coverage estimator (ACE) and Chao1 showed that there
was no significant difference in OTU richness between
MDD patients (young and middle-aged, respectively)
and their respective HCs. However, the OPLS-DA
model built with young HCs and young MDD patients
showed an obvious difference in microbial abundances
between these two groups (Figure 2A). The relative
abundances of Firmicutes and Bacteroidetes were
Figure 1. Comparison of alpha diversity between HCs and MDD
patients. (A, B) ACE and Chao1 indexes showed no significant
differences between young HCs (n=27) and young MDD
patients (n=25); (C, D) ACE and Chao1 indexes showed no
significant differences

between middle-aged HCs (n=44) and middle-aged MDD
patients (n=45).
significantly decreased and increased, respectively, in
young MDD patients as compared with young HCs
(Figure 2B). Meanwhile, the OPLS-DA model built
with middle-aged HCs and middle-aged MDD patients
showed an obvious difference in microbial abundances
between these two groups (Figure 3A). The relative
abundances of Bacteroidetes and Actinobacteria were
middle-aged MDD patients as compared with middle-
aged HCs (Figure 3B).
Key discriminatory OTUs
In order to find out the gut microbiota primarily
responsible for the separation between MDD patients
(young and middle-aged, respectively) and their

respective HCs, the Random Forests classifier was used.
A total of 92 OTUs responsible for the separation
between young MDD patients and young HCs were
identified (Figure 4). These OTUs were mainly assigned
to the Families of Bacteroidaceae, Clostridiaceae_1,
Figure 2. 16S rRNA gene sequencing reveals changes to
microbial abundances in young MDD patients. (A) OPLS-DA
model
showed an obvious difference in microbial abundances between
the two groups (HCs, n=27; MDD, (n=25); (B) the relative
abundances of
Firmicutes and Bacteroidetes were significantly changed in
young MDD patients (n=25) as compared with young HCs
(n=27).
microbial abundances in middle-aged MDD patients. (A) OPLS-
DA
model showed an obvious difference in microbial abundances
between the two groups (HCs, n=44; MDD, (n=45); (B) the
relative abundances
of Bacteroidetes and Actinobacteria were significantly changed
in middle-aged MDD patients (n=45) as compared with middle-
aged HCs
(n=44).

Coriobacteriaceae, Erysipelotrichaceae, Lachnospiraceae,
Peptostreptococcaceae and Ruminococcaceae.
Meanwhile, a total of 122 OTUs responsible for the
separation between middle-aged MDD patients and
middle-aged HCs were identified (Figure 5). These OTUs
were mainly assigned to the Families of Lachnospiraceae,
Coriobacteriaceae, Streptococcaceae, Prevotellaceae,
Bacteroidaceae, Eubacteriaceae, Actinomycetaceae,
Sutterellaceae, Acidaminococcaceae, Erysipelotrichaceae,
Ruminococcaceae, and Porphyromonadaceae.
Figure 4. Heatmap of discriminative OTUs abundances between
young HCs (n=27) and young MDD patients (n=25).
Figure 5. Heatmap of discriminative OTUs abundances between
middle-aged HCs (n=44) and middle-aged MDD patients
(n=45).

Differentially abundant bacterial taxa
Differentially abundant bacterial taxa responsible for
the differences between MDD patients (young and
middle-aged, respectively) and their respective HCs
were identified by the metagenomic Linear
Discriminant Analysis (LDA) Effect Size (LEfSe)
approach (LDA score>2.0 and p-value<0.05). In total,
six bacterial taxa with statistically significant and
biologically consistent differences in young MDD
patients were identified (Figure 6). Meanwhile, fifteen
bacterial taxa with statistically significant and
biologically consistent differences in middle-aged MDD
patients were identified (Figure 7). In addition, using
Figure 6. Differentially abundant features identified by LEfSe
that characterize significant differences between young HCs

(n=27) and young MDD patients (n=25).
Figure 7. Differentially abundant features identified by LEfSe
that characterize significant differences between middle-aged
HCs (n=44) and middle-aged MDD patients (n=45).
the receiver operating characteristic (ROC) curve
analysis, we found that Clostridium_sensu_stricto,
Clostridium_XI and Clostridium_XVIII showed good
diagnostic performance (area under the curve (AUC)
>0.7) in diagnosing young MDD patients (Figure 8A–
8C). We also found that Anaerostipes, Streptococcus,
Blautia, Faecalibacterium and Roseburia showed good
diagnostic performance (AUC>0.7) in diagnosing
middle-aged MDD patients (Figure 8D–8H).
Effects of age on microbial abundances
Using the LEfSe approach, we identified four

differentially abundant bacterial taxa (the Family
level) between young HCs and middle-aged HCs
(Streptococcaceae, Coriobacteriaceae, Carnobacteriaceae
and Clostridiales_Incertae_Sedis_XIII) (Figure 9A);
we also identified six differentially abundant bacterial
taxa (the Family level) between young MDD patients
and middle-aged MDD patients (Prevotellaceae,
Acidaminococcaceae, Veillonellaceae Peptostrep-
tococcaceae, Lachnospiraceae and Ruminococcaceae)
(Figure 9B). Meanwhile, using the LEfSe approach, we
identified five differentially abundant bacterial taxa (the
Genus level) between young HCs and middle-aged HCs
(Streptococcus, Veillonella, Granulicatella, Collinsella
and Megamonas) (Figure 10A). All these bacterial
taxa were significantly decreased in middle-aged
HCs; we also identified nine differentially abundant
bacterial taxa (the Genus level) between young MDD
patients and middle-aged MDD patients (Megamonas,

Prevotella, Phascolarctobacterium, Anaerostipes,
Clostridium_XVIII, Gordonibacter, Eggerthella,
Clostridium_XI and Turicibacter) (Figure 10B).
Effects of medication on microbial abundances
To determinate the homogeneity of gut microbiota
composition between medicated and non-medicated
MDD patients, we firstly used the middle-aged HCs and
non-medicated middle-aged MDD patients to built
OPLS-DA model (Figure 11A). The results showed that
41 of the 44 middle-aged HCs and 30 of the 31 non-
medicated middle-aged MDD patients were correctly
diagnosed by the OPLS-DA model. Then, we used the
built model to predict class membership of 14
medicated middle-aged MDD patients. The T-predicted
scatter plot showed that 11 of the 14 medicated middle-
aged MDD patients were correctly predicted (Figure
11B). These finding indicated that the gut microbiota

composition of non-medicated middle-aged MDD
patients were distinct from middle-aged HCs, but not
from medicated middle-aged MDD patients.
DISCUSSION
Individuals in the different phases of life cycle (named
children, young, middle-aged and elderly) present
different biological characteristics and disease risks
[20]. Understanding the different characteristics of
patients in particular age phases could be facilitated to
prevent and treat diseases. According to the World
Health Organization reported, the prevalence rates of
depression vary by age, peaking in older adulthood. It
also occurs in children, but at a lower level compared
with older age groups. Here, we conducted this work to
investigate how the gut microbiota composition
changed in different age phases of MDD patients, and
found some age-specific differential gut microbiota in

Figure 8. Differential taxa (at the genus level) with AUC>0.7 in
diagnosing MDD patients from HCs. (A–C) the diagnostic
performances of three taxa in diagnosing young MDD patients
(n=25) from young HCs (n=27); (D–H) the diagnostic
performances of five taxa
in diagnosing middle-aged MDD patients (n=45) from middle-
aged HCs (n=44).
microbial abundances at family level (Mean±SEM). (A) the
abundances
of four taxonomic levels were significantly changed between
young HCs (n=27) and middle-aged HCs (n=44); (B) the
abundances of six
taxonomic levels were significantly changed between young
MDD patients (n=25) and middle-aged MDD patients (n=45).
microbial abundances at genus level (Mean±SEM). (A) the
abundances
of five taxonomic levels were significantly changed between
young HCs (n=27) and middle-aged HCs (n=44); (B) the
abundances of nine
taxonomic levels were significantly changed between young
MDD patients (n=25) and middle-aged MDD patients (n=45).

MDD patients. Our results could provide a new
perspective on exploring the pathogenesis of MDD.
Many previous studies focused on the effects of gut
microbiota on brain functions [21, 22]. However, few
studies have taken the effects of age on gut microbiota
into consideration when exploring the pathogenesis of
MDD. Our previous study found that the relative
MDD patients as compared with HCs [15]. But, in this
study, we found that the relative abundances of
Firmicutes and Bacteroidetes were significantly
decreased and increased, respectively, in young MDD
patients as compared with young HCs, and the relative

middle-aged MDD patients as compared with middle-
aged HCs. This disparity might be caused by the
different age structures. Meanwhile, only 35 key
discriminatory OTUs were significantly changed in both
young (92 key discriminatory OTUs) and middle-aged
(127 key discriminatory OTUs) MDD patients.
Moreover, the differentially abundant bacterial taxa in
young and middle-aged MDD patients were totally
different at both Family level and Genus level. These
results demonstrated that it was necessary to identify the
age-specific differential gut microbiota in patients with
MDD.
As far as we known, gut microbiota composition and its
function could be easily influenced by many factor,
such as gender, age, life experiences, dietary habit and
genetics. Mariat et al reported that the

Firmicutes/Bacteroidetes ratio of the human microbiota
could change with age [23]. Interestingly, here we
found that the relative abundance of Firmicutes was
significantly decreased in young MDD patients, but not
in middle-aged MDD patients; the relative abundance of
Bacteroidetes was significantly increased and
decreased, respectively, in young and middle-aged
MDD patients. In our previous studies, we did not
analyze the potential effects of medication on gut
microbiota composition in MDD patients [15, 16]. Here,
due to the small samples of young group, we only used
the middle-aged group to analyze the effects of
Figure 11. Assessment of gut microbiota composition in non-
medicated and medicated middle-aged MDD patients. (A)
middle-aged HCs (n=44) and non-medicated middle-aged MDD
patients (n=31) were effectively separated by the built OPLS-
DA model; (B) 14
medicated middle-aged MDD patients were correctly predicted
by the model.

medication on the gut microbiota composition. The
results showed that the medication seemed to have little
effects on gut microbiota composition in MDD patients.
However, our findings had to be cautiously interpreted
due to the relatively small samples using to analyze the
effects of medication on gut microbiota composition.
The relative abundance of genus Clostridium_XVIII
was not found to be significantly different between
MDD patients and HCs in our previous study [15].
However, in this study, we found that the relative
abundance of genus Clostridium_XVIII was
significantly decreased in young MDD patients
compared with young HCs, while increased in middle-
aged MDD patients compared with middle-aged HCs.
The reason of this disparity might be that age could
significantly affect the relative abundance of genus

Clostridium_XVIII in MDD patients, but not HCs: i)
compared to young MDD patients, the middle-aged
MDD patients had a significantly higher relative
abundance of genus Clostridium_XVIII; and ii) the
relative abundance of genus Clostridium_XVIII was
similar between young and middle-aged HCs.
Meanwhile, we found that the relative abundance of
genus Megamonas was significantly decreased in both
middle-aged HCs and middle-aged MDD patients
compared to their respective young populations. In
addition, most of differential bacterial taxa were
significantly decreased in middle-aged HCs compared
with young HCs, but only about half of differential
bacterial taxa were significantly decreased in middle-
aged MDD patients compared with young MDD
patients. Lozupone et al. reported that gut microbiota
could not only simply determine the certain host
characteristics, but also respond to signals from host via

multiple feedback loops [24]. Therefore, our results
suggested that age might have the different effects on
the gut microbiota composition of HCs and MDD
patients, and should always be considered in
investigating the relationship between MDD and gut
microbiota.
Limitations should be mentioned here. Firstly, the
number of HCs and MDD patients was relatively small,
and future works were still needed to further study and
support our results. Secondly, we only explored the age-
specific differential changes on gut microbiota
composition in patients with MDD; future studies
should further investigate the functions of these
identified differential gut microbiota using
metagenomic technology. Thirdly, all included subjects
were from the same site and ethnicity; thus, the
potential site- and ethnic-specific biases in microbial
phenotypes could not be ruled out, which might limit

the applicability of our results [25–28]. Fourthly, only
young and middle-aged groups were recruited, future
studies should recruit old-aged group and children
group to further identify the age-specific differential gut
microbiota in the different phases of life cycle. Fifthly,
we only investigated the differences in gut microbiota
between HCs and MDD patients on phylum level,
family level and genus level. Future studies were
needed to further explore the differences on other
levels, such as class level and species level. Sixthly, we
did not collect information on smoking, a factor which
could influence the gut microbiota composition. Future
studies were needed to analyze how the smoking
influenced the gut microbiota composition in the
different phases of life cycle of subjects. Finally, we
found that the medication status of subjects could not
significantly affect our results. However, limited by the
relatively small samples, this conclusion was needed

future studies to further validate.
In conclusion, in this study, we found that there were
age-specific differential changes on gut microbiota
composition in patients with MDD, and identified some
age-specific differentially abundant bacterial taxa in
MDD patients. Our findings would provide a novel
perspective to uncover the pathogenesis underlying
MDD, and potential gut-mediated therapies for MDD
patients. Limited by the small number of subjects, the
results of the present study were needed future studies
to validate and support.
MATERIALS AND METHODS
Subject recruitment
This study was approved by the Ethical Committee of
Chongqing Medical University and conformed to the
provisions of the Declaration of Helsinki. In total, there

were 27 young HCs (aged 18-29 years) and 25 young
MDD outpatients (aged 18-29 years) in the young
group; there were 44 middle-aged HCs (aged 30-59
years) and 45 middle-aged MDD outpatients (aged 30-
59 years) in the middle-aged group. Most of MDD
patients were first-episode drug-naïve depressed
subjects. There were only seven young MDD patients
and 14 middle-aged MDD patients receiving
medications. The detailed information of these included
subjects was described in Table 1. All HCs were
recruited from the Medical Examination Center of
Chongqing Medical University, and all MDD patients
were recruited from the psychiatric center of Chongqing
Medical University. MDD patients were screened in the
baseline interview by two experienced psychiatrists
using the DSM-IV (Diagnostic and Statistical Manual
of Mental Disorders, 4th Edition)-based Composite
International Diagnostic Interview (CIDI, version2.1).

The Hamilton Depression Rating Scale (HDRS) was
used to assess the depressive symptoms of each patient,
Table 1. Demographic and clinical characteristics of MDD
patients and HCsa.
Young group (18-29 years) Middle-aged group (30-59 years)
HC MDD p-value HC MDD p-value
Sample Size 27 25 – 44 45 –
Age (years)c 24.96±2.31 24.0±3.74 0.26 47.16±8.07 44.96±7.76
0.19
Sex (female/male) 19/8 18/7 0.89 34/10 31/14 0.37
BMI 21.53±2.37 22.13±2.24 0.35 23.23±2.33 22.64±2.64 0.26
Medication (Y/N) 0/27 7/18 – 0/44 14/31 –
HDRS scores 0.29±0.61 22.64±3.18 <0.00001 0.34±0.74
23.0±4.61 <0.00001
aAbbreviations: HDRS: Hamilton Depression Rating Scale;
HCs: healthy controls; MDD: major depressive disorder; BMI:
body
mass index.

and those patients with HDRS score >=17 were
included. Meanwhile, MDD patients were excluded if
they had other mental disorders, illicit drug use or
substance abuse, and were pregnant or menstrual
women. HCs were excluded if they were with mental
disorders, illicit drug use or systemic medical illness.
All the included subjects provided written informed
consent before sample collection.
16s rRNA gene sequencing
We used the standard PowerSoil kit protocol to extract
the bacterial genomic DNA from the fecal samples.
Briefly, we thawed the frozen fecal samples on ice and
pulverized the samples with a pestle and mortar in
liquid nitrogen. After adding MoBio lysis buffer into
the samples and mixing them, the suspensions were
centrifuged. The obtained supernatant was moved into
the MoBio Garnet bead tubes containing MoBio buffer.

Subsequently, we used the Roche 454 sequencing (454
Life Sciences Roche, Branford, PA, USA) to extract the
bacterial genomic DNA. The extracted V3-V5 regions
of 16S rRNA gene were polymerase chain reaction-
amplified with bar-coded universal primers containing
linker sequences for pyrosequencing [29].
The Mothur 1.31.2 (http://www.mothur.org/) was used
to quality-filtered the obtained raw sequences to
identify unique reads [30]. Raw sequences met any one
of the following criteria were excluded: i) less than
200bp or greater than 1000bp; ii) contained any
ambiguous bases, primer mismatches, or barcode
mismatches; and iii) homopolymer runs exceeding six
bases. The remaining sequences were assigned to
operational taxonomic units (OTUs) with 97%
threshold, and then taxonomically classified according
to Ribosomal Database Project (RDP) reference

database [31]. We used these taxonomies to construct
the summaries of the taxonomic distributions of OTUs,
and then calculated the relative abundances of gut
microbiota at different levels. The abovementioned
procedure and most of data were from our previous
studies [15, 16].
Statistical analysis
Richness was one of the two most commonly used alpha
diversity measurements. Here, we used two different
parameters (Chao1 and ACE) to estimate the OTU
richness [32, 33]. The orthogonal partial least squares
discriminant analysis (OPLS-DA) was a multivariate
method, which was used to remove extraneous variance
(unrelated to the group) from the sequencing datasets. The
LEfSe was a new analytical method for discovering the
metagenomic biomarker by class comparison. The
bacterial taxa with LDA score>2.0 were viewed as the

differentially abundant bacterial taxa responsible for the
differences between different groups. Here, both OPLS-
DA [34, 35] and LEfSe were used to reduce the
dimensionality of datasets and identify the differentially
abundant bacterial taxa (the Family level and Genus level)
that could be used to characterize the significant
differences between HCs and MDD patients. Meanwhile,
we used the Random Forest algorithm to identify the
critical discriminatory OTUs. The ROC curve analysis
was used to assess the diagnostic performance of these
identified differential bacterial taxa. The AUC was the
evaluation index. Finally, we used the LEfSe to reveal the
changes of microbial abundances at Family level and
Genus level in HCs and MDD patients, respectively.
ACKNOWLEDGMENTS
Our sincere gratitude is extended to Professors Delan
Yang and Hua Hu from Psychiatric Center of the First

Affiliated Hospital of Chongqing Medical University
for their efforts in sample collection.
CONFLICTS OF INTEREST
The authors declare no financial or other conflicts of
interest.
http://www.mothur.org/
FUNDING
This work was supported by the National Key R&D
Program of China (2017YFA0505700), the Non-profit
Central Research Institute Fund of Chinese Academy of
Medical Sciences (2019PT320002300), the Natural
Science Foundation Project of China (81820108015,
81701360, 81601208, 81601207), the Chongqing
Science and Technology Commission
(cstc2017jcyjAX0377), the Chongqing Yuzhong

District Science and Technology Commission
(20190115), and supported by the fund from the Joint
International Research Laboratory of Reproduction &
Development, Institute of Life Sciences, Chongqing
Medical University, Chongqing, China, and also
supported by the Scientific Research and Innovation
Experiment Project of Chongqing Medical University
(CXSY201862, CXSY201863).
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RESEARCH Open Access
Associations between gut microbiota and
Alzheimer’s disease, major depressive
disorder, and schizophrenia
Zhenhuang Zhuang1, Ruotong Yang1, Wenxiu Wang1, Lu
Qi2,3* and Tao Huang1,4,5,6*
Abstract

Background: Growing evidence has shown that alterations in the
gut microbiota composition were associated
with a variety of neuropsychiatric conditions. However, whether
such associations reflect causality remains
unknown. We aimed to reveal the causal relationships among
gut microbiota, metabolites, and neuropsychiatric
disorders including Alzheimer’s disease (AD), major depressive
disorder (MDD), and schizophrenia (SCZ).
Methods: A two-sample bi-directional Mendelian randomization
analysis was performed by using genetic variants from
genome-wide association studies as instrumental variables for
gut microbiota, metabolites, AD, MDD, and SCZ, respectively.
Results: We found suggestive associations of host-genetic-
driven increase in Blautia (OR, 0.88; 95%CI, 0.79–0.99;
P = 0.028) and elevated γ-aminobutyric acid (GABA) (0.96;
0.92–1.00; P = 0.034), a downstream product of
Blautia-dependent arginine metabolism, with a lower risk of
AD. Genetically increased Enterobacteriaceae family
and Enterobacteriales order were potentially associated with a
higher risk of SCZ (1.09; 1.00–1.18; P = 0.048),
while Gammaproteobacteria class (0.90; 0.83–0.98; P = 0.011)
was related to a lower risk for SCZ. Gut
production of serotonin was potentially associated with an
increased risk of SCZ (1.07; 1.00–1.15; P = 0.047).
Furthermore, genetically increased Bacilli class was related to a
higher risk of MDD (1.07; 1.02–1.12; P = 0.010).
In the other direction, neuropsychiatric disorders altered gut
microbiota composition.
Conclusions: These data for the first time provide evidence of
potential causal links between gut microbiome
and AD, MDD, and SCZ. GABA and serotonin may play an
important role in gut microbiota-host crosstalk in
AD and SCZ, respectively. Further investigations in

understanding the underlying mechanisms of associations
between gut microbiota and AD, MDD, and SCZ are required.
Keywords: Gut microbiota, Neuropsychiatric disorder,
Mendelian randomization, Genetic association, Causality
Background
The human intestine comprises a very complex group of
gut microbiota, which influence the risk of neuropsychiatric
disorders [1, 2]. Accumulating evidence has suggested that
microbiota metabolites such as neurotransmitters and
short-chain fatty acids (SCFAs) may play a central role in
microbiota-host crosstalk that regulates the brain function
and behavior [3, 4]. Therefore, to understand the mechan-
ism of the gut-brain axis in neuropsychiatric disorders may
have clinical benefits.
Observational studies, most of case-control designs, have
shown differences in the composition of the gut microbiota
between healthy individuals and patients with neuropsychi-
atric disorders such as Alzheimer’s disease (AD), major
depression disorder (MDD), and schizophrenia (SCZ);
© The Author(s). 2020 Open Access This article is licensed
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(http://creativecommons.org/publicdomain/zero/1.0/) applies to
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data made available in this article, unless otherwise stated in a
credit line to the data.
* Correspondence: [email protected]; [email protected]
2Department of Epidemiology, School of Public Health and
Tropical
Medicine, Tulane University, New Orleans, LA, USA
1Department of Epidemiology & Biostatistics, School of Public
Health, Peking
University, 38 Xueyuan Road, Beijing 100191, China
Full list of author information is available at the end of the
article
Zhuang et al. Journal of Neuroinflammation (2020)
17:288
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01961-8&domain=pdf
http://orcid.org/0000-0002-0328-1368
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however, such associations substantially differed across
studies [5–7]. Noteworthy, genome-based metabolic mod-

eling of the human gut microbiota revealed that several
genera have predictive capability to produce or consume
neurotransmitters (called microbial neurotransmitters) such
as γ-aminobutyric acid (GABA) and serotonin [8, 9], which
have been consistently shown to played a key role in the
regulation of brain function [10, 11]. A meta-analysis of 35
observational studies reported that increased GABA levels
were associated with a lower risk of AD [12]. In addition, a
previous study (n = 40) reported that plasma serotonin was
lower and platelet serotonin was higher in SCZ patients
compared with controls [13], while another study showed
that lower platelet serotonin concentrations were associated
with depressive symptoms of SCZ (n = 364) [14]. There is
no doubt that these small observational studies were sus-
ceptible to confounding bias and reverse causation. It is
crucial to elucidate whether such associations reflect causal
relations or spurious correlations due to bias.
Mendelian randomization (MR), which overcomes the
bias due to confounding and reverse causation above-
mentioned, has been widely used to assess causal rela-
tionships by exploiting genetic variants as instrumental
variables of the exposure [15]. Recent genetic studies
have demonstrated that the host genetic variants influ-
ence the gut microbiota composition [16–18]. Thus,
such findings allowed us to deploy an MR approach to
infer the mutually causal relations of gut microbiota and
metabolites with neuropsychiatric disorders.
Therefore, we for the first time applied a two-sample
bi-directional MR approach to detect causal relation-
ships among gut microbiota, metabolites, and diverse
forms of neuropsychiatric disorders including AD, SCZ,
and MDD.
Methods

Study design overview
We employed a two-sample bi-directional MR approach
to investigate the causal relationships among gut micro-
biota, metabolites, and AD, MDD, or SCZ using
summary-level data from large genome-wide association
studies (GWASs) for gut microbiota and AD, MDD, or
SCZ. Ethical approval for each study included in the MR
analysis can be found in the original articles [19–23].
Data sources and instruments
Gut microbiota
We leveraged summary statistics from a GWAS of gut
microbiota conducted among two independent but geo-
graphically matched cohorts of European ancestry (n =
1812) using 16S rRNA gene sequencing (Table 1) [19],
which yielded a total of 38 and 374 identified phyla and
genera respectively. The GWAS defined a “core measur-
able microbiota” after removing rare bacteria and
investigating associations between host genetic variants
and specific bacterial traits, including 40 operational
taxonomic units (OTUs) and 58 taxa ranging from the
genus to the phylum level. Accordingly, the GWAS fur-
ther identified 54 genome-wide significant associations
involving 40 loci and 22 bacterial traits (meta-analysis P
< 5 × 10−8). We selected single nucleotide polymor-
phisms (SNPs) at thresholds for genome-wide signifi-
cance (P < 5 × 10−8) from this GWASs as genetic
instruments (Table S1).
Gut microbial metabolites
Considering the important roles of gut microbiota-
derived metabolites in microbiota-host crosstalk in the
brain function and behavior, we further chose key me-
tabolites with available GWAS, including propionic acid,
β-hydroxybutyric acid (BHB), serotonin, GABA, tri-

methylamine N-oxide (TMAO), betaine, choline, and
carnitine. These gut microbial metabolites play crucial
roles in maintaining a healthy neuropsychiatric function,
and if dysregulated, potentially causally linked to neuro-
psychiatric disorders according to previous studies [3,
24, 25]. We searched PubMed for GWASs of the gut
metabolites and leveraged summary-level data from a re-
cent GWAS of the human metabolome conducted
among 2076 participants of the Framingham Heart
Study (Table 1) [20]. Since few loci identified by gut me-
tabolite GWAS have reached the level of genome-wide
significance, we only selected SNPs at thresholds for
suggestive genome-wide significance (P < 1 × 10−5) from
the GWAS for each metabolite (Table S2).
Neuropsychiatric disorders
We searched PubMed for GWASs of the neuropsychi-
atric disorders and identified SNPs with genome-wide
significant (P < 5 × 10−8) associations for AD [21], MDD
[22], and SCZ [23], respectively (Table 1, Table S3).
Summarized data for AD were obtained from the Inter-
national Genomics of Alzheimer’s Project (IGAP), in-
cluding 25,580 AD cases and 48,466 controls, and the
analysis was adjusted for age, sex, and principal compo-
nents when necessary [21]. Genetic associations for
MDD were obtained from Psychiatric Genomics Consor-
tium 29 (PGC29) including135,458 MDD cases and 344,
901 controls, using sex and age as covariates [22]. Gen-
etic associations for SCZ were obtained from a meta-
analysis of Sweden and PGC including 13,833 SCZ cases
and 18,310 controls [23]. Detailed information on diag-
nostic criteria for AD, MDD, and SCZ are provided in
Table S4. These GWASs identified 19 SNPs for AD, 44
SNPs for MDD, and 24 SNPs for SCZ (P < 5 × 10−8), re-
spectively (Table S3).

17:288 Page 2 of 9
Statistical analysis
For instrumental variables, we only selected independent
genetic variants which are not in linkage disequilibrium
(LD) (defined as r2 < 0.1) with other genetic variants
based on European ancestry reference data from the
1000 Genomes Project. We chose the variant with the
lowest P value for association with the exposure when
genetic variants were in LD. Moreover, for SNPs that
were not available in GWASs of the outcome, we used
the LD proxy search on the online platform (https://
snipa.helmholtz-muenchen.de/snipa3/index.php/) to re-
place them with the proxy SNPs identified in high-LD
(r2 > 0.8) or discard them if the proxies were not avail -
able. Power calculations for the MR study were con-
ducted based on the website: mRnd (http://cnsgenomics.
com/shiny/mRnd/).
We combined MR estimates by using inverse variance
weighting (IVW) as primary method. Weighted mode,
weighted median, and MR-Egger methods were used as
sensitivity analyses. Detailed information about the MR
methods mentioned above has been explained previously
[26, 27]. The MR-Egger method examined for unknown
horizontal pleiotropy as indicated by a non-zero inter-
cept value. We also applied leave-one-SNP-out approach
assessing the effects of removing these SNPs from the
MR analysis to rule out potential pleiotropic effects. Ef-
fect estimates are reported in beta values for the con-
tinuous outcome and ORs (95% CIs) for binary
outcome. Bonferroni correction was used to adjust for
multiple comparisons, giving a cutoff of P = 7.6 × 10−4

for the causal effect of gut microbiota on disorders and a
cutoff of P = 1.7 × 10−4 for reverse causation.
The MR analyses were conducted in the R version
3.5.1 computing environment (http://www.r-project.org)
using the TwoSampleMR package (R project for Statis-
tical Computing). This package harmonized effect of the
exposure and outcome data sets including combined in-
formation on SNPs, including phenotypes, effect alleles,
effect allele frequencies, effect sizes, and standard errors
for each SNP. In addition, we assumed that all alleles are
presented on the forward strand in harmonization. In
conclusion, the bi-directional MR results using the full
set of selected SNPs.
Results
Associations of gut microbiota and metabolites with
neuropsychiatric disorders
We found suggestive evidence of a protective effect of
the host-genetic-driven increase in Blautia on the risk of
AD (per relative abundance: OR, 0.88; 95% CI, 0.79–
0.99; P = 0.028) (Fig. 1, Figure S1). Importantly, we fur-
ther observed suggestive evidence that genetically ele-
vated gut metabolite GABA was associated with a lower
risk of AD (per 10 units: 0.96; 0.92–1.00; P = 0.034)
(Figs. 1 and 2).
Furthermore, the host-genetic-driven increases in En-
terobacteriaceae family and Enterobacteriales order were
potentially related to a higher risk of SCZ (1.09; 1.00–
1.18; P = 0.048), while Gammaproteobacteria class was
related to a lower risk of SCZ (0.90; 0.83–0.98; P =
0.011) (Fig. 1, Figure S1). Interestingly, gut production of
serotonin was potentially associated with a higher risk of

SCZ (1.07; 1.00–1.15; P = 0.047) (Figs. 1 and 3). In
addition, we found suggestive association of the host-
genetic-driven increase in Bacilli class with a higher risk
of MDD (1.07; 1.02–1.12; P = 0.010) (Fig. 1, Figure S1).
Sensitivity analysis yielded similar results for the causal
effects of gut microbiota on neuropsychiatric disorders, and
no horizontal pleiotropy or outliers were observed (Tables
S5 and S6). No significant results were found for any of
other selected gut microbiota or metabolites with neuro-
psychiatric disorders (Table S7). MR power calculation
showed strong power to detect significant (P < 7.6 × 10−4)
causal effect (OR = 1.2) for most of gut microbiota with the
risk of AD, MDD, and SCZ, respectively (Table S8).
Associations of neuropsychiatric disorders with gut
microbiota
In the opposite direction, we applied the MR method to
investigate the causal relationship of neuropsychiatric
Table 1 Description of gut microbiota, metabolites, and
neuropsychiatric disorders
Traits Consortium or study Sample size Populations Journal
Year
Gut
Gut microbiota PopGen/FoCus 1812 individuals European Nat
Genet. 2016
Gut metabolites FHS 2076 individuals European Cell Metab.
2013
Neuropsychiatric disorders

Alzheimer’s disease IGAPa 25,580 cases and 48,466 controls
European Nat Genet. 2013
Major depression disorder
PGC29/deCODE/GenScotland/GERA/iPSYCH/UK
Biobank/23andMeD
135,458 cases and 344,901 controls European Nat Genet. 2018
Schizophrenia Sweden/PGC 21,246 cases and 38,072 controls
European Nat Genet. 2013
FoCus Food-Chain Plus, GERA Genetic Epidemiology Research
on Adult Health and Aging, PGC Psychiatric Genomics
Consortium
a IGAP includes the Alzheimer’s Disease Genetics Consortium
(ADGC), the Cohorts for Heart and Aging Research in Genomic
Epidemiology consortium (CHARGE),
the European Alzheimer’s disease Initiative (EADI), and the
Genetic and Environmental Risk in Alzheimer’s disease
consortium (GERAD)
17:288 Page 3 of 9
https://snipa.helmholtz-muenchen.de/snipa3/index.php
https://snipa.helmholtz-muenchen.de/snipa3/index.php
http://cnsgenomics.com/shiny/mRnd/
http://cnsgenomics.com/shiny/mRnd/
http://www.r-project.org
disorders with gut microbiota. We found a suggestive as-
sociation of AD with lower relative abundance of Erysi -
pelotrichaceae family, Erysipelotrichales order, and
Erysipelotrichia class (per 1-unit odds ratio: Beta±SE, −

0.274 ± 0.090; P = 0.003) and higher relative abundance
of unclassified Porphyromonadaceae (0.351 ± 0.170; P =
0.040) (Fig. 1, Table S9). Additionally, MDD was associ -
ated with higher relative abundance of unclassified Clos-
tridiales (0.577 ± 0.241; P = 0.017), OTU16802
Bacteroides (0.842 ± 0.386; P = 0.029), and unclassified
Prevotellaceae (0.978 ± 0.464; P = 0.035) (Fig. 1, Table
S9). We further identified that SCZ was nominally re-
lated to 2 genera, including higher relative abundance of
OTU10589 unclassified Enterobacteriaceae (0.457 ±
0.220; P = 0.037) and lower relative abundance of un-
classified Erysipelotrichaceae (− 0.248 ± -0.019; P =
0.045) (Fig. 1, Table S9).
Associations were almost consistent in sensitivity ana-
lyses using the weighted mode and weighted median
methods. The MR-Egger method showed directional
pleiotropy in the analysis of association between MDD
and OTU16802 Bacteroides (P = 0.022) but not in any
other potential significant associations (Table S9). How-
ever, we had limited power (all less than 50%) to test sig-
nificant (P < 1.7 × 10−4) causal effect (Beta = 0.5) of the
risk of AD, MDD, and SCZ on specific gut microbiota
(data not shown), possibly due to small sample size of
the gut microbiota GWAS.
Discussion
In this two-sample bi-directional MR study, we found
suggestive evidence of causal relationships of Blautia
with AD, of Enterobacteriaceae family, Enterobacteriales
order, and Gammaproteobacteria class with SCZ, and of
Bacilli class with MDD. More importantly, several neu-
rotransmitters such as GABA and serotonin produced
by gut microbiota were also potentially linked to the
risks of neuropsychiatric disorders, implying their im-

portant roles in microbiota-host crosstalk in the brain
function and behavior. In the other direction, our results
suggested that neuropsychiatric disorders, including AD,
SCZ, and MDD might alter the composition of gut
microbiota.
Microbiota-gut-brain communication has been shown
to play a key role in cognitive function [2]. However,
animal studies regarding the effects of Blautia genus on
AD have yielded conflicting results, but extrapolating
these findings to human beings is challenging [28, 29]. A
cohort study (n = 108) reported that decreased propor-
tion of Blautia hansenii was associated with a higher
risk of AD [30], while two case-control studies observed
that Blautia were more abundant in AD patients [5, 31].
Fig. 1 Schematic representation of the present study,
highlighting for each step of the study design and the
significant results obtained. We
aimed to estimate causal relationships between gut microbiota
(98 individual bacterial traits) and neuropsychiatric disorders
(Alzheimer’s disease,
major depression disorder, and schizophrenia) using a bi-
directional Mendelian randomization (MR) approach (step 1).
Then, we performed a two-
sample MR analysis to identify which microbiota metabolites
associated with these disorders (step 2). Finally, we identified
14 individual bacterial
traits and 2 gut metabolites to be associated with these
disorders. GABA, γ-aminobutyric acid; SCFA, short-chain fatty
acids
17:288 Page 4 of 9

Although the direction of associations between Blautia
and the risk of AD substantially differed across studies,
one consistent finding was that gut microbial neuro-
transmitter GABA, a downstream product of Blautia-
dependent arginine metabolism, was related to a reduced
risk of AD. Notably, lower levels of gut product of
GABA were observed in patients with AD in several
case-control studies [32, 33]. In this bi-directional MR
study, our results for the first time provide evidence of a
causal relationship between relative abundance of Blau-
tia and AD. More importantly, we demonstrated that el-
evated GABA was potentially associated with a lower
risk of AD. Our findings supported previous meta-
analysis of 35 observational studies which suggested that
GABA level in AD were significantly lower than that of
controls [12]. Our findings suggest that GABA produced
by gut microbiota may play an important role in
microbiota-host crosstalk in the brain function and be-
havior. Although not significant, our findings show very
similar association directions for Blautia with MDD and
SCZ. Our findings are in line with recent studies which
indicated that decreased Blautia was associated with an
increased risk of autistic spectrum disorder (ASD), sug-
gesting a general change associated with psychiatric dis-
orders [34].
There are many potential pathways linking specific gut
microbiota to AD, among which metabolites produced
by gut microbiota may play an important role. It is
worth noting that GABA, as a primary inhibitory neuro-
transmitter in the human central nervous system (CNS),
has been shown to shape neurological processes and
cognition [35]. Recent evidence has demonstrated that
GABAergic functions could be an essential factor in the
whole stage of AD pathogenesis which seemed to be

more resistant to neurodegenerative changes in aged
brain [36, 37]. Our MR results that increased GABA
levels was potentially associated with a lower risk of AD
lent further support to the hypotheses. The biological
mechanisms of GABA production include degradation
of putrescine, decarboxylation of glutamate, or from ar-
ginine or ornithine [8]. Interestingly, the genus Blautia
has shown a strong correlation with arginine metabolism
[38], which may be involved in AD pathogenesis by
regulating its downstream products such as GABA, sup-
porting the potential pathway [39]. Since AD does not
break out suddenly but develops through a long pro-
dromal phase instead, it is plausible that our findings may
be potentially effective in early interventions of such dis -
order in the future by targeting the microbiota (e.g., gut
microbiota transplantation, psychobiotics, or antibiotics).
Fig. 2 Causal effect of GABA with the risk of AD. a Schematic
representation of the MR analysis results: genetically
determined higher GABA
plasma levels were potentially associated with a lower risk of
AD. b The odds ratios (95% confidence interval) for AD per 10
units increase in
GABA, as estimated in the inverse-variance weighted, weighted
mode, weighted median, and MR-Egger MR analysis. The
intercept of MR-Egger
can be interpreted as a test of overall unbalanced horizontal
pleiotropy. c The scatter plot represents instruments association
including AD
associations (y-axis) against instrument GABA associations (x-
axis). The tunnel plot represents instrument precision (i.e.,
instrument AD regression
coefficients divided by the correspondent instrument GABA
SEs) (y-axis) against individual instrument ratio estimates in
log odds ratio of AD (x-
axis). βIV indicates odds ratio estimate per 1-ln 10 units

increment in GABA levels. AD, Alzheimer’s disease; OR, odds
ratio; CI, confidence interval;
SNP, single-nucleotide polymorphism; SE, standard error; IVW,
inverse variance weighted
17:288 Page 5 of 9
Recently, Enterobacteriales family and Gammaproteo-
bacteria class have been identified to be important bio-
markers of SCZ in recent cross-sectional studies,
consistent with our findings [6, 40]. Furthermore, a case-
control study (n = 364) identified a strong relationship
of lower platelet serotonin concentrations with depres-
sive symptoms of SCZ [14]. However, available evidence
is still largely inadequate since observational studies
mainly rely on self-reported information and are suscep-
tible to confounding (e.g., diet and health status) and re-
verse causation bias. Ertugrul et al. observed plasma
serotonin increased while platelet serotonin decreased in
SCZ patients after clinical treatments, which was incon-
sistent with our findings [13]. In addition, our results
support the finding that increased Bacilli is potentially
associated with a higher risk of MDD, possibly involving
dopamine metabolism which might play a role in the
major symptoms of MDD [41, 42]. A meta-analysis of
RCTs showed that probiotics, typically including Lacto-
bacillus and Bifidobacterium, had some benefit for
MDD, but we found no associations for these micro-
biota, possibly due to the synergistic effect of gut micro-
biome so that the influence of a particular taxon may be
different from multiple taxa [43]. Furthermore, these
clinical trials might draw biased conclusions because of
small sample sizes (ranging from 17 to 110) or short-

term effects (ranging from 3 to 24 weeks). Therefore, a
large and long-term RCT in a well-characterized popula-
tion using probiotic capsules containing specific micro-
biota might provide further evidence for the gut-brain
axis in these disorders. Importantly, epidemiological
study indicated that elevated Enterobacteriales was also
associated with a higher risk of ASD, suggesting that the
same changes in intestinal microbiota composition
might lead to different outcomes due to gene-gene inter-
actions and gene-environment interactions [44]. Al-
though our results showed no significant association for
Gammaproteobacteria and MDD, animal models found
increased levels of Gammaproteobacteria were also asso-
ciated with higher MDD risk and fluoxetine treatment
was effective, implying strong correlations between gut
microbiota and anxiety- and depression-like behaviors
[45].
The serotonin hypothesis of SCZ originated from earl-
ier studies of interactions between the hallucinogenic
drug D-lysergic acid diethylamide and serotonin in per-
ipheral systems. However, direct evidence of serotoner-
gic dysfunction in the pathogenesis of SCZ remains
unclear [46]. According to the principle of brain plasti -
city, glutamate signals are destroyed by serotonergic
overdrive, leading to neuronal hypometabolism, synaptic
atrophy, and gray matter loss in the end [47]. Our find-
ings that genetically increased serotonin levels was po-
tentially related to a high risk of SCZ using a MR
Fig. 3 Causal effect of serotonin with the risk of SCZ. a
Schematic representation of the MR analysis results: genetically
determined higher
serotonin plasma levels were potentially associated with a
higher risk of SCZ. b The odds ratios (95% confidence interval)

for SCZ per 10 units
increase in serotonin, as estimated in the inverse-variance
weighted, weighted mode, weighted median, and MR-Egger MR
analysis. The intercept
of MR-Egger can be interpreted as a test of overall unbalanced
horizontal pleiotropy. c The scatter plot represents instruments
association
including SCZ associations (y-axis) against instrument
serotonin associations (x-axis). The tunnel plot represents
instrument precision (i.e.,
instrument SCZ regression coefficients divided by the
correspondent instrument serotonin SEs) (y-axis) against
individual instrument ratio
estimates in log odds ratio of SCZ (x-axis). βIV indicates odds
ratio estimate per 1-ln 10 units increment in serotonin levels.
SCZ, schizophrenia
17:288 Page 6 of 9
approach supported such hypothesis. Importantly, En-
terobacteriaceae family and Enterobacteriales order can
produce SCFAs (e.g., acetic acid and formic acid) in
carbohydrate fermentation, thus inducing serotonin bio-
synthesis by enterochromaffin cells which are the major
producers of serotonin, and ultimately increasing the
risk of SCZ [48, 49]. Our novel findings highlighted the
potentially important role of gut microbiota-related neu-
rotransmitters in effective and benign therapies of psy-
chiatric disorders.
Furthermore, we also found that neuropsychiatric disor-
ders might alter the composition of gut microbiota. Our
findings were consistent with a small case-control study (n

= 50) suggesting that Erysipelotrichaceae family were all
less abundant in patients with AD [5]. An observational
study showed that Porphyromonadaceae were associated
with poor cognitive performance, partly consistent with our
results [50]. However, the results from animal studies are
conflicting. Although several animal studies suggested that
anti-AD microbes, such as Erysipelotrichiaceae, decreased
in mouse models with AD, and Porphyromonadaceae in-
creased in aged mice [28, 51], other animal studies showed
that the relative abundance of Erysipelotrichiaceae was
positively correlated with AD [52, 53]. Therefore, the asso-
ciation of neuropsychiatric disorders with specific gut
microbiota requires further study. It is universally accepted
that the CNS modulates gut microbiota compositions
mainly through hypothalamic-pituitary-adrenal (HPA) axis,
or classical neurotransmitters liberated by neuronal efferent
activation, which explains the microbiota-host crosstalk in
neuropsychiatric disorders from another direction [54].
Additionally, it is plausible that alterations in gut
microbiota and related metabolites would lead to a sys-
temic change in inflammation that may contribute to
the neuroinflammation in AD, MDD, and SCZ. Increas-
ing evidence suggests that bacteria populating the gut
microbiome may excrete large quantities of lipopolysac-
charides and amyloids, resulting in the pathogenesis of
AD during aging when the permeability of gastrointes-
tinal tract epithelium or blood-brain barrier increases
[55]. Recent research has indicated that gut inflamma-
tion can induce activation of microglia and the kynure-
nine pathway, which activate systemic inflammation-
inducing depressive or schizophrenic symptoms [56, 57].
Therefore, more studies are required to explore the
mechanisms underlying the relationships of inflamma-
tion with the gut microbiota-brain axis and its relations
with AD, MDD and SCZ.

Strengths of the present study included the bi-directional
MR design and the use of summary-level data from thus far
the largest GWASs. This design generally avoided bias due
to reverse causation and confounding to obtain accurate
results under MR assumptions. In addition, consistent re-
sults from several sensitivity analyses including the use of
weighted mode, weighted median, and MR-egger methods
indicate robustness of our findings. However, several limita-
tions merit consideration. First, our results did not survive
a strict Bonferroni correction adjusting for multiple com-
parisons, whereas as a hypothesis-driven approach, the MR
study with some biological evidence was used to test epide-
miologically established associations, regardless of Bonfer -
roni corrected P values. Second, we used limited number of
gut microbiota SNPs as instrumental variables; we cannot
exclude that our findings might have been affected by weak
instrument bias, although all genetic instruments were as-
sociated with the exposure (F-statistic > 10). Third, statis-
tical power was limited for associations of neuropsychiatric
disorders with gut microbiota, so we cannot exclude type II
error as an explanation for the null results completely. Lar -
ger GWASs of gut microbiota are required to provide suffi -
cient statistical power. However, the power was strong
enough for the effect of gut microbiota on these disorders,
which was our main findings in the present study. Fourth,
our results were restricted to European ancestry. Replica-
tion with functionally relevant genetic prediction of gut
microbiota is warranted given the substantial difference in
gut microbiota composition among different populations.
Fifth, the 16S rRNA gene sequencing only permit reso-
lution from the genus to the phylum level rather than at a
more specific level, resulting in biased results if some spe-
cific species contributed to neuropsychiatric disorders. Fi -
nally, gut microbiota might be influenced by environmental

factors such as dietary habits or health status, which led to
lower variance explained by genetic instruments. However,
we could not test whether genetic instruments are associ-
ated with these confounders such as diet or lifestyle infor -
mation in the present study where such information is not
available.
Conclusions
In summary, our findings supported several potential as-
sociations between specific gut microbiota and neuro-
psychiatric disorders and highlighted the important roles
of microbial neurotransmitters such as GABA and sero-
tonin in microbiota-host crosstalk in neuropsychiatric
disorders. Further investigations in understanding the
underlying mechanisms of gut microbiota in the devel-
opment of neuropsychiatric disorders are required.
Supplementary information
Supplementary information accompanies this paper at
https://doi.org/10.
1186/s12974-020-01961-8.
Additional file 1: Figure S1. Odds ratio for association of
genetically
predicted gut microbiota with neuropsychological diseases.
Table S1.
Characteristics of selected SNPs for core gut microbiota. Table
S2.
Characteristics of selected SNPs for gut metabolites. Table S3.
Characteristics of selected SNPs for neuropsychological
diseases. Table
S4. Description of the diagnostic assessment for
neuropsychological
17:288 Page 7 of 9

https://doi.org/10.1186/s12974-020-01961-8
https://doi.org/10.1186/s12974-020-01961-8
diseases. Table S5. Associations between genetically predicted
gut
microbiota and neuropsychological diseases in sensitivity
analyses. Table
S6. Associations between genetically predicted gut microbiota
and
neuropsychological diseases in a leave-one-out approach. Table
S7. As-
sociations between genetically predicted metabolites and
neuropsycho-
logical diseases using IVW method. Table S8. MR Power
calculation for
detecting significant (P < 7.6 × 10-4) causal effect (OR = 1.2)
of gut micro-
biome on the risk of AD, MDD, and SCZ. Table S9. Effect
estimates for
association of genetically predicted neuropsychological diseases
with gut
microbiota using four Mendelian randomization methods.
Abbreviations
AD: Alzheimer’s disease; MDD: Major depressive disorder;
SCZ: Schizophrenia;
MR: Mendelian randomization; GWAS: Genome-wide
association study;
GABA: γ-Aminobutyric acid; SCFA: Short-chain fatty acid;
BHB: β-
Hydroxybutyric acid; TMAO: Trimethylamine N-oxide; SNP:
Single nucleotide
polymorphism; IGAP: International Genomics of Alzheimer’s
Project;

PGC29: Psychiatric Genomics Consortium 29; LD: Linkage
disequilibrium;
IVW: Inverse variance weighting; CNS: Central nervous
system;
HPA: Hypothalamic-pituitary-adrenal; ASD: Autistic spectrum
disorder
Acknowledgements
The PopGen 2.0 network (P2N) is supported by a grant from the
German
Federal Ministry for Education and Research (01EY1103). We
thank Drs. Andre
Franke and Wolfgang Lieb for sharing the GWAS summary data
for beta
diversity and bacterial abundance from published paper (Nat
Genet. 2016
Nov; 48(11): 1396-1406.)
Authors’ contributions
ZZ, LQ, and TH designed the research. ZZ and TH had full
access to all the
data in the study and take responsibility for the integrity of the
data and the
accuracy of the data analysis. ZZ, LQ, and TH wrote the paper
and performed
the data analysis. All authors contributed to the statistical
analysis, critically
reviewed the manuscript during the writing process, and
approved the final
version to be published. ZZ and TH are the guarantors for the
study.
Funding
The study was supported by grants from the National Key
Research and
Development Project (2019YFC2003400), the Peking University

Start-up Grant
(BMU2018YJ002), High-performance Computing Platform of
Peking Univer-
sity, and the China-Canada Key Lab of Nutrition and Health at
Beijing Tech-
nology and Business University- Grant: 88442Y0033. The
funding organization
had no role in the preparation of the manuscript.
Availability of data and materials
All data used in the present study were obtained from genome-
wide associ-
ation study summary statistics which were publicly released by
genetic
consortia.
Ethics approval and consent to participate
Contributing studies received ethical approval from their
respective
institutional review boards.
Consent for publication
Not applicable.
Competing interests
All authors declare no support from companies for the
submitted work; no
relationships with companies that might have an interest in the
submitted
work in the previous 3 years; no spouses, partners, or children
that have
financial relationships that may be relevant to the submitted
work; and no
non-financial interests that may be relevant to the submitted
work.

Author details
1Department of Epidemiology & Biostatistics, School of Public
Health, Peking
University, 38 Xueyuan Road, Beijing 100191, China.
2Department of
Epidemiology, School of Public Health and Tropical Medicine,
Tulane
University, New Orleans, LA, USA. 3Department of Nutrition,
Harvard T.H.
Chan School of Public Health, Boston, MA, USA. 4Department
of Global
Health, School of Public Health, Peking University, Beijing
100191, China. 5Key
Laboratory of Molecular Cardiovascular Sciences (Peking
University), Ministry
of Education, Beijing 100191, China. 6Center for Intelligent
Public Health,
Institute for Artificial Intelligence, Peking University, Beijing
100191, China.
Received: 2 July 2020 Accepted: 23 September 2020
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Publisher’s Note
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17:288 Page 9 of 9
AbstractBackgroundMethodsResultsConclusionsBackgroundMet
hodsStudy design overviewData sources and instrumentsGut
microbiotaGut microbial metabolitesNeuropsychiatric
disordersStatistical analysisResultsAssociations of gut
microbiota and metabolites with neuropsychiatric
disordersAssociations of neuropsychiatric disorders with gut
microbiotaDiscussionConclusionsSupplementary
informationAbbreviationsAcknowledgementsAuthors’
contributionsFundingAvailability of data and materialsEthics
approval and consent to participateConsent for
publicationCompeting interestsAuthor
detailsReferencesPublisher’s Note
OPEN
ORIGINAL ARTICLE

Inflammasome signaling affects anxiety- and depressive-like
behavior and gut microbiome composition
M-L Wong1,2,9,10, A Inserra1,2,9, MD Lewis1,2, CA
Mastronardi3, L Leong4,5, J Choo4,5, S Kentish6, P Xie7,10, M
Morrison8, SL Wesselingh4,5,
GB Rogers4,5,10 and J Licinio1,2,10
The inflammasome is hypothesized to be a key mediator of the
response to physiological and psychological stressors, and its
dysregulation may be implicated in major depressive disorder.
Inflammasome activation causes the maturation of caspase-1
and
activation of interleukin (IL)-1β and IL-18, two
proinflammatory cytokines involved in
neuroimmunomodulation, neuroinflammation
and neurodegeneration. In this study, C57BL/6 mice with
genetic deficiency or pharmacological inhibition of caspase-1
were
screened for anxiety- and depressive-like behaviors, and
locomotion at baseline and after chronic stress. We found that
genetic
deficiency of caspase-1 decreased depressive- and anxiety-like
behaviors, and conversely increased locomotor activity and
skills.
Caspase-1 deficiency also prevented the exacerbation of
depressive-like behaviors following chronic stress. Furthermore,
pharmacological caspase-1 antagonism with minocycline
ameliorated stress-induced depressive-like behavior in wild-
type mice.
Interestingly, chronic stress or pharmacological inhibition of
caspase-1 per se altered the fecal microbiome in a very similar
manner.
When stressed mice were treated with minocycline, the
observed gut microbiota changes included increase in relative
abundance
of Akkermansia spp. and Blautia spp., which are compatible

with beneficial effects of attenuated inflammation and rebalance
of gut
microbiota, respectively, and the increment in Lachnospiracea
abundance was consistent with microbiota changes of caspase-1
deficiency. Our results suggest that the protective effect of
caspase-1 inhibition involves the modulation of the relationship
between stress and gut microbiota composition, and establishes
the basis for a gut microbiota–inflammasome–brain axis,
whereby
the gut microbiota via inflammasome signaling modulate
pathways that will alter brain function, and affect depressive-
and
anxiety-like behaviors. Our data also suggest that further
elucidation of the gut microbiota–inflammasome–brain axis may
offer
novel therapeutic targets for psychiatric disorders.
Molecular Psychiatry (2016) 21, 797–805;
doi:10.1038/mp.2016.46; published online 19 April 2016
INTRODUCTION
Increasing evidence suggests an involvement of neuroinflamma-
tory pathways in the etiopathophysiology of major depressive
disorder (MDD) and antidepressant response.1,2 Depressive
symptoms are underlined by increased levels of
proinflammatory
cytokines (that is, interleukin (IL)-1β and IL-6), decreased
levels of
anti-inflammatory cytokines (that is, IL-4 and IL-10) and are
associated with polymorphisms in inflammation-related
genes.3–5
IL-1 receptor type-I and its ligands are expressed in brain areas
relevant to stress response,6–8 and IL-1β signaling is
fundamental
in mediating the deleterious neurobehavioral and

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